Beyond the Encoder: Joint Encoder-Decoder Contrastive Pre-Training Improves Dense Prediction

Abstract

Contrastive learning methods in self-supervised settings have primarily focused on pre-training encoders, while decoders are typically introduced and trained separately for downstream dense prediction tasks. However, this conventional approach overlooks the potential benefits of jointly pre-training both encoder and decoder. In this paper, we propose DeCon, an efficient encoder-decoder self-supervised learning (SSL) framework that supports joint contrastive pre-training. We first extend existing SSL architectures to accommodate diverse decoders and their corresponding contrastive losses. Then, we introduce a weighted encoder-decoder contrastive loss with non-competing objectives to enable the joint pre-training of encoder-decoder architectures. By adapting a contrastive SSL framework for dense prediction, DeCon establishes consistent state-of-the-art performance on most of the evaluated tasks when pre-trained on Imagenet-1K, COCO and COCO+. Notably, when pre-training a ResNet-50 encoder on COCO dataset, DeCon improves COCO object detection and instance segmentation compared to the baseline framework by +0.37 AP and +0.32 AP, respectively, and boosts semantic segmentation by +1.42 mIoU on Pascal VOC and by +0.50 mIoU on Cityscapes. These improvements generalize across recent backbones, decoders, datasets, and dense tasks beyond segmentation and object detection, and persist in out-of-domain scenarios, including limited-data settings, demonstrating that joint pre-training significantly enhances representation quality for dense prediction. Code is available at https://github.com/sebquetin/DeCon.git.